Comparison of advanced troposphere models for aiding reduction of PPP convergence time in Australia

Deo, Manoj; El‐Mowafy, Ahmed

doi:10.1080/14498596.2018.1472046

Cited by 7 publications

(6 citation statements)

References 32 publications

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“…Probabilities of correct detection P CD and statistical overlap P ol for the two soft constraints A and D (Cluster 5) with σ c = 10mm and for α = 0.001.Similar effects of the relaxation of the constraints on the performance of the IDS in case of two soft constraints are verified in case of three soft constraints, as can be seen in theFigures 9,10, 11 and 12. …”

supporting

confidence: 62%

“…Such constraints are a prior knowledge embedded into a model to avoid a trivial solution; to guarantee the stability of estimates; to improve the precision and accuracy of the results by reducing the number of unknown parameters or accordingly by increasing the redundancy of the system; and to mitigate (or even estimate) a possible systematic effect [1][2][3]. For example, [4] adopted constraints to determine the transponder coordinates in a problem of combining satellite positioning (GNSS) of a surface platform with acoustic ranging to seafloor transponders; [5][6][7][8][9][10] have used constraints to model the atmospheric effects on GNSS signals; and [11] have imposed the constraints of predicted satellite clocks to improve the precise orbit determination (POD) processing during maneuvers.…”

Section: Introductionmentioning

confidence: 99%

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Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?

Rofatto¹,

Matsuoka²,

Klein³

et al. 2020

Preprint

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In this paper we evaluate the effects of hard and soft constraints on the Iterative Data Snooping (IDS), an iterative outlier elimination procedure. Here, the measurements of a levelling geodetic network were classified according to the local redundancy and maximum absolute correlation between the outlier test statistics, referred to as clusters. We highlight that the larger the relaxation of the constraints, the higher the sensitivity indicators MDB (Minimal Detectable Bias) and MIB (Minimal Identifiable Bias) for both the clustering of measurements and the clustering of constraints. There are circumstances that increase the family-wise error rate (FWE) of the test statistics, increase the performance of the IDS. Under a scenario of soft constraints, one should set out at least three soft constraints in order to identify an outlier in the constraints. In general, hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. In that process, one should opt to set out the redundant hard constraints. After identifying and removing possible outliers, the soft constraints should be employed to propagate their uncertainties to the model parameters during the process of least-squares estimation.

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supporting

confidence: 62%

Section: Introductionmentioning

confidence: 99%

Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?

Rofatto¹,

Matsuoka²,

Klein³

et al. 2020

Preprint

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“…Numerical weather models (NWMs) are important data sources for space geodetic techniques; NWM-derived tropospheric delays are widely used in various geodetic techniques, such as Very Long Baseline Interferometry (VLBI) (Landskron and Böhm 2018a), Satellite Laser Ranging (SLR) (Mendes et al 2002), Satellite Altimetry (SA) (Vieira et al 2022), Interferometric Synthetic Aperture Radar (InSAR) (Foster et al 2006), and Global Navigation Satellite System (GNSS) (Lu et al 2017;Wilgan et al 2017). Numerous scholars have found and validated that NWM enhances the wet tropospheric correction retrieval of SA (Vieira et al 2022), improves the troposphere delays in SLR (Boisits et al 2020), provides more accurate gradient information to VLBI (Hofmeister and Böhm 2017), reduces GNSS positioning convergence time, and exhibits better overall robustness (Lu et al 2016(Lu et al , 2017Vaclavovic et al 2017;Deo and El-Mowafy 2018). Multiple tropospheric delay models (Schüler 2014;Li et al 2014Li et al , 2015Yang et al 2021), temperature and pressure models (Boehm et al , 2015Lagler et al 2013;Landskron and Böhm 2018b), weighted mean temperature models (Zhu et al 2022), mapping function models (Urquhart et al 2014;Zus et al 2015), and tropospheric gradient models have been developed based on different NWMs; they are typically used for correction or as a priori inputs in the above techniques (Wang et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Characteristic differences in tropospheric delay between Nevada Geodetic Laboratory products and NWM ray-tracing

et al. 2023

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Numerical weather models (NWMs) are important data sources for space geodetic techniques. Additionally, the Global Navigation Satellite System (GNSS) provides many observations to continuously improve and enhance the NWM. Existing comparative analysis experiments on NWM tropospheric and GNSS tropospheric delays suffer from being conducted in highly specific regions with limited spatial coverage; furthermore, the length of time for the experiment is too short for analyzing seasonal characteristics, and the insufficient number of stations limits spatial density, making it difficult to obtain the equipment-dependent distribution characteristics. After strict quality control and data preprocessing, we have calculated and compared the bias and standard deviation of tropospheric delay for approximately 7000 selected Nevada Geodetic Laboratory (NGL) GNSS stations in 2020 with the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) hourly ray-traced tropospheric delay for the same group of stations. Characterizations in time, space, and linkage to receivers and antennas reveal positive biases of approximately 4 mm in the NGL zenith tropospheric delay (ZTD) relative to the NWM ZTD over most of the globe; moreover, there is a seasonal amplitude reaching 6 mm in the bias, and an antenna-related mean bias of approximately 1.6 mm in the NGL tropospheric delay. The obtained results can be used to provide a priori tropospheric delays with appropriate uncertainties; additionally, they can be applied to assess the suitability of using NWMs for real-time positioning solutions.

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“…The tropospheric delay can also be obtained from the global or regional numerical weather model (NWM). Research showed that the accuracy of the tropospheric delay obtained from NWM is better than that predicted by empirical models [32][33][34]. Usually, the ZHD is fixed with a model-predicted value and the ZWD is estimated as an unknown parameter in the PPP data-processing.…”

Section: Introductionmentioning

confidence: 99%

An investigation of real-time GPS/GLONASS single-frequency precise point positioning and its atmospheric mitigation strategies

Sun

Zhang

et al. 2021

Meas. Sci. Technol.

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One of the challenges in the innovative application of global navigation satellite systems (GNSS) lies in real-time single-frequency precise point positioning (RT-SFPPP). The well-known problems associated with SFPPP are its slow convergence and lower positioning accuracy due to the effects of various errors inherited by the GNSS positioning, including the atmospheric error. In order to mitigate the two above-mentioned problems, several scenarios for the reduction of the atmospheric delays in RT-SFPPP, including the ionospheric constraint and tropospheric constraint, were investigated in this research. The performance of the RT-SFPPP utilizing the above-mentioned methods was evaluated using one-week observations from multi-GNSS experiment stations in a simulated kinematic mode. Results showed that the convergence time of the RT-SFPPP was significantly shortened when the slant ionospheric delays derived from the real-time ionospheric products provided by Centre National d’Études Spatiales were applied as the pseudo-observations. Results also showed that the convergence time of GPS + GLONASS RT-SFPPP can be reduced by modeling the frequency-dependent part of the GLONASS receiver uncalibrated code delay as a quadratic polynomial function of GLONASS frequency number. The a priori zenith wet delays (ZWDs) derived from forecast Vienna Mapping Functions 3 (VMF3_FC) were compared to the reference ZWDs from globally distributed radiosonde stations. The mean root mean square error of the a priori ZWDs resulting from VMF3_FC at 381 radiosonde stations in 2020 was 1.53 cm compared to the radiosonde-based ZWDs. When the ZWDs derived from VMF3_FC were used as pseudo-observations in the position estimation system, the convergence time for the vertical positioning reaching a 0.3 m accuracy was considerably shortened.

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Comparison of advanced troposphere models for aiding reduction of PPP convergence time in Australia

Cited by 7 publications

References 32 publications

Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?

Which Are the Effects of Hard and Soft Equality Constraints on the Iterative Outlier Elimination Procedure?

Characteristic differences in tropospheric delay between Nevada Geodetic Laboratory products and NWM ray-tracing

An investigation of real-time GPS/GLONASS single-frequency precise point positioning and its atmospheric mitigation strategies

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